This section shows some graphics about the processed data. The data consists in three .csv files:

  1. metadata.csv

  2. hplc.csv

  3. absorption.csv

sample_id 438116 was duplicated and removed from the data.

Notes

  • Will be using depth instead of pressure to characterize the depth of the measurements.

  • Will be only using observations measured in the first 2 meters of the water column. All the following graphs will be using data measured between 0-2 meters.

Mismatch between metadata, HPLC and absorption

Now, this table shows the metadata that have no HPLC values associated.

These are the absorption spectra with no HPLC association.

First visualizations

Most of the data is from the Scotian Shelf, Labrador Sea and Scotian Shelf and Slope.

The number of observation is also relatively stable through the years.

Bioregions

Observations were classified into bioregions based on the following criteria (where position is south if latitude < 48):

bathymetry > -300 & position == "North" ~ "Labrador & Greenland Shelves (LGS)"
bathymetry <= -300 & position == "North" ~ "Labrador Sea Basin (LSB)"
bathymetry >= -300 & position == "South" & yday <= 180 ~ "Scotian Shelf Spring (SSSp)"
bathymetry >= -300 & position == "South" & yday > 180 ~ "Scotian Shelf Fall (SSFa)"
bathymetry < -300 & position == "South"~ "Northwest Atlantic Basin ocean (NAB)"

HPLC

  • I also noticed there are a lot of 0 in the HPLC data. After discussions, they are true 0 and not missing values.

  • Some HPLC sample_id were not numeric (ex.: FL002), I removed them from the data.

  • There are hplchla and hplcchla variables in the data. I have merge both columns into one because I suspect it is an error and should be the same variable.

  • but19 was summed as but19 + butlike.

  • hex19 was summed as hex19 + hexlike + hexlike2.

  • I have calculated aphy_specific using hplcchla.

  • Here is an attempt to visualize the seasonal cycle of few pigments. I have calculated the average pigment concentration and then the average total concentration to finally calculate the relative average contribution of each pigment.

Absorption

  • Here is how I named absorption data:

    • anap is the data contained in files with detritus in their name.
    • ap is the data contained in files with particulate in their name.
    • aphy is the data contained in files with phytoplankton in their name.
  • Absorption spectra with any values <= 0 between 350 and 400 nm have been removed.

  • Absorption spectra with aphy(440) < aphy(410) have been removed (possible problem with pigment extraction).

ap

aphy

aphy specific

We can observe that there is a specific absorption gradient among bioregions.

anap

Calculating snap

The fit was done for data between 380 and 730 nm, excluding the 400–480 and 620–710 nm ranges to avoid any residual pigment absorption that might still have been present after sodium hypochlorite treatment (Babin 2003).

A R2 of 0.90 was used to filter out bad models.

Preliminary analysis

Seasonal cycle

  • The next figure shows how many observations there are for each year of the day. We can see that there are temporal gaps in the data. For example, in Labrador & Greenland Shelves (LGS), there are only data in May/June. It is not enough to get insights on the seasonal cycles. At the moment, I am not sure how to deal with that.

  • A first attempt can be by using boxplots to see if there are differences among months and bioregions.

  • Another way to visualize the same data.

  • We can also look at the seasonal variability by bioregion. There are interesting patterns in the data. For instance, in Northwest Atlantic Basin ocean (NAB) we can clearly see the seasonal pattern of hex19.

Phytoplankton population model

Explore and try to understand the method of Emmanuel that aims to decompose absorption spectra into two populations.

Bricaud 1995

In this section, I put our data in the context of the paper by Bricaud et al. (1995).

  • In table 2, they provide coefficients to fit \(a^*_\phi\) as a function of chlorophyll-a between 400 and 700 nm. The next figure shows some randomly selected spectral profiles of \(a^*_\phi\) and compare them with those modeled using the coefficients presented in Bricaud et al. (1995).

  • The next figure shows \(a^*_\phi\) modeled at different wavelengths using the following equation:

\[ a^*_\phi(\lambda) = A(\lambda)\times\text{chl-a}^{-B(\lambda)} \]

Fucoxanthin bining

Relationships between fucoxanthin relative content and absorption

Based on the linear relationships in the previous graph, this shows the R2 as a function of the wavelength.

Apparent visible wavelength (AVW)

In this section we explore how the Apparent visible wavelength (AVW) could be used to describe the spectral shape of phytoplankton absorption. The AVW index is calculated following (Vandermeulen et al. 2020).

This graph shows all the phytoplankton absorption spectra colored by the value of the calculated AVW.

Figures for the manuscript

Figure 1

Figure 2

Attention: These numbers are based on the total number of reported stations. For example, in 2019, there are a total of 217 stations, but only 71 of them have absorption measurements.

Figure 3

  • There are some unusual bumps around 530 nm. We should have a closer look at the raw data.

  • Within each panel, we can see the averaged spectra and a subset of 100 randomly selected spectra by bioregion and type of absorption.

Figure 4

Figure 5

The observed nonlinearity between Chl_a and the ratio of phytoplankton absorption aph (443)/aph (670) indicating the packaging effect and changes in the intercellular composition of pigments.

Figure 6

Figure 7

Figure 8

Figure 9

Appendix

Appendix 1

Numbers at the right represent: mean (min - max).

Appendix 2

This aphy* ratio was found to correlate with phytoplankton cell size. That is cool to see that PAAW correlates with it! Using PAAW to infer aphy* means that we do not have to measure chla (HPLC or other ways).

References

Babin, Marcel. 2003. “Variations in the Light Absorption Coefficients of Phytoplankton, Nonalgal Particles, and Dissolved Organic Matter in Coastal Waters Around Europe.” Journal of Geophysical Research 108 (C7): 3211. https://doi.org/10.1029/2001JC000882.
Bricaud, Annick, Marcel Babin, André Morel, and Hervé Claustre. 1995. “Variability in the Chlorophyll-Specific Absorption Coefficients of Natural Phytoplankton: Analysis and Parameterization.” Journal of Geophysical Research 100 (C7): 13321. https://doi.org/10.1029/95JC00463.
Vandermeulen, Ryan A., Antonio Mannino, Susanne E. Craig, and P. Jeremy Werdell. 2020. “150 Shades of Green: Using the Full Spectrum of Remote Sensing Reflectance to Elucidate Color Shifts in the Ocean.” Remote Sensing of Environment 247 (September): 111900. https://doi.org/10.1016/j.rse.2020.111900.